Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Introduction to the Monte Carlo method (M Ljungberg). Variance reduction techniques (D R Haynor). Anthropomorphic phantoms (G Zubal). General Monte Carlo codes for use in medical radiation physics (P Andreo and M Ljungberg). An introduction to scintillation detector physics (P D Esser). The scintillation camera - basic principles (S-E Strand). The SIMSET program (T Lewellen). Vectorized Monte Carlo code for modelling photon transport in nuclear medicine (M F Smith). Positron emission tomography - basic principles (T Ohlsson and K Erlandsson). The SIMSPECT simulation system (M J Belanger et al). Monte Carlo simulation of photon transport in gamma camera collimators (D J de Vries and S C Moore). The SIMIND Monte Carlo program (M Ljungberg). Monte Carlo in SPECT scatter correction (K F Koral). Design of a collimator for imaging ^T111In (S C Moore et al). Estimation of the lung regions from Compton scatter data in SPECT (M A King and T-S Pan). The Monte Carlo method applied in other areas of SPECT imaging (M Ljungberg). Positron emission tomography: basic principles (K Erlandsson and T Ohlsson). PETSIM: Monte Carlo simulation of positron imaging systems (C J Thompson and Y Picard). Monte Carlo in quantitative 3D PET: Scatter (M Dahlbom and L Eriksson). The Monte Carlo method in other topics of nuclear medicine and medical physics (M Ljungberg). Contributors: Dr Dan DeVries, U Mass, Worcester Dr S C Moore, V A Medical Center, MA C J Thompson, McGill U, Canada Dr Pedro Andreo, IAEA and Stockholm, Vienna, Austria Dr Ken Koral, U Michigan Ann Arbor, Dr S P Mueller, Essen University Hospital, Germany Dr Marie Kijewski, Brigham and Women's Hospital, Boston, US George Zubal, Yale U, School of Medicine Dr Mike King, U Mass Medical School, Worcester MA, US Drs Miyaoka and Harrison, U Washington Medical Center, Seattle, US Dr M Dahlbom, UCLA School of Medicine, US L Eriksson, Karolinska Institute (so is Andreo) Sweden
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it